Data streams and data synopses for massive data sets

Authors:
Yossi Matias
Affiliations:
Tel Aviv University, HyperRoll Inc., Stanford University
Venue:
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2005

Citing 4
Cited 0

The space complexity of approximating the frequency moments

Journal of Computer and System Sciences
Synopsis data structures for massive data sets

Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
External memory algorithms and data structures: dealing with massive data

ACM Computing Surveys (CSUR)
Models and issues in data stream systems

Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

With the proliferation of data intensive applications, it has become necessary to develop new techniques to handle massive data sets. Traditional algorithmic techniques and data structures are not always suitable to handle the amount of data that is required and the fact that the data often streams by and cannot be accessed again. A field of research established over the past decade is that of handling massive data sets using data synopses, and developing algorithmic techniques for data stream models. We will discuss some of the research work that has been done in the field, and provide a decades’ perspective to data synopses and data streams.